Enterprises today accumulate huge quantities of data that is often noisy and unstructured in nature, making data cleansing an important task. Data cleansing refers to standardizing data from different sources to a common format so that data can be better utilized. Most of the enterprise data cleansing models are rule-based and involve a lot of manual effort. Writing data quality rules is a tedious task and often results in creation of erroneous rules because of the ambiguities that the data presents. A robust data cleansing model should be capable of handling a wide variety of records, and often the model is dependant on the choice of the sample records knowledge engineers use to write the rules.